Data integration Maximise media ROI by Henry Eccles Admap, September 2011

1.
Data integration: Maximise media ROI
Henry Eccles
Admap
September 2011

2.
Title: Data integration: Maximise media ROI
Author(s): Henry Eccles
Source: Admap
Issue: September 2011
Data integration: Maximise media ROI
Henry Eccles
Google EMEA
Spanish retailer PC City's data analytics showed that 10% of sales came from online research but purchased in-store,
prompting a media review that gained a 6% sales uplift for the same spend
How to optimally allocate investments across media channels is the main goal of all business and is becoming increasingly
complex. Traditionally, defining an optimal marketing mix has centred around a marketeers' knowledge and experience in their
field, reinforced with analytics focused on the linear economics between ‘paid’ media and business success. In today's digital
and data-rich world however, it's increasingly possible to make decisions that are far more effective and accountable.
PC City, part of the DSGi group in Spain, was keen to do exactly this. They had a solid understanding of the performance and
profitability of their store operation, but only a limited feel for how their various marketing levers, such as their website, pricing
strategy, ad spend, store distribution etc impacted on sales performance.
To address this, they worked with independent analytics consultancies (MarketShare and Conento) to build a set of
customised models for each of their main product lines (desktop, laptop and netbook). Each model incorporated data from
traditional marketing activities (investment, GRPs, adstock) as well as rich digital data from consumer-initiated contacts and
other outcomes believed to be an integral part of the business ecosystem. Broadly, the project broke down into four distinct
phases:
Downloaded from warc.com
2

3.
l Consultation to understand the business, its position in the market and its revenue model.
l Agreement on the areas of analytical focus, data availability and what outcomes to model that best represented their
business economy.
l The design and build of an econometric modelling framework to interrogate all marketing and revenue levers.
l Multiplicative logarithmic time series models or ‘systems of equations’ built to uncover how marketing functions across
online and offline, via both direct and indirect pathways.
All models were corrected to eliminate misattribution (last click bias), before historical ROI analysis of each marketing vehicle
and optimal investment allocation for future marketing and sales periods was derived.
The process of designing and building this specific infrastructure or any attribution model has two important components. What
are the model and associated assumptions and how ‘rich’ is the data to fit the model. Today's digital environment gives
marketers access to this rich (highly variable) data. PC City's approach was the integration of this data alongside sound
assumptions of how media works today in driving both sales on and offline, as well as an understanding of the intermediate
outcomes of value in a consumer purchase path.
As an example, search advertising was and is a growing part of PC City's media mix. It was, however, not just this increasing
level of investment that was modelled against sales, but the specifics of its keyword performance (e.g. click-through-rate, cost-
per-click, impression share, position on search engine results page, etc.). The key treatment of search (AdWords data) in this
instance was the understanding that it works both as an independent variable of web visits and subsequent sales (both on and
offline), but also as a dependent variable of other marketing activities. It was therefore modelled accordingly and
independently of its monetary investment.
One of the common failings of some market mix modelling is to ignore this new interplay of push and pull that digital data
allows us to measure. A failure to understand the source driving consumer-initiated contacts will almost certainly cause a
degree of over-attribution to the most recent touchpoint before an outcome.
Modelling just Adwords data in this way to quantify search, however, is also incomplete. AdWords data does deliver an
approximation of consumer intent, but its limitations lie in that most advertisers have insufficient campaigns in terms of breadth
and coverage to effectively capture all keyword volume relating to their business.
PC City was no exception in this regard. To overcome this, PC City used Google natural search query volume data (available
from Google Insights For Search) in its model as a signal of consumer interest. This data, in turn, was modelled alongside its
AdWords, website and sales data. The degree to which these three variables (query volume, search ad impressions, clicks
and web visits) are serially correlated delivers an indication of how effectively PC City were in capturing online consumer
interest.
It was from this analysis that PC City understood that they were underspent in paid search and that there was value to be
gained from a reallocated media mix. This same rationale and optimisation analysis was applied across all on and offline data
sets on the foundation that feedback or interaction effects can exist between all sales channels and media (Figure 1).
THE RESULTS
Downloaded from warc.com
3

4.
The most striking finding for PC City was the role of the web and, specifically, its own website in driving offline in-store
purchase. It already knew that 4% of revenue came via online sales and a further 2% through online reservations, but the
analytics uncovered a further 10% of total sales coming by way of the ‘online to store’ or ROPO (research online purchase
offline) effect - this a bigger sales driver than price promotion and TV adspend. Overall, it was found that marketing was
driving 38% of the total PC City business.
The process made PC City aware not just of the insights that could come from a ‘rearview mirror’ look at their business, but
how the analytics could be predictive of future outcomes. Optimal recommendations of marketing resource across channels
pointed investment away from traditional broadcast media to online, and paid search specifically. The implication: a 6%
increase in unit sales with the same marketing investment.
The challenge for PC City and, in fact, all marketeers is to embrace and see opportunity in the changing media landscape and
consumer behaviour. Marketing strategy and analytics should at all times be surrounded by and rooted in relevant data.
Sources of data are only likely to become richer, more numerous and more complex as technology drives more ‘connected’
consumers. Additionally, as social media data becomes increasingly mainstream, it demands not just quantification of volume,
but also a review of sentiment to gauge its value to businesses.
In today's connected world, smarter marketing strategy has to be rooted heavily on the rich data that digital provides.
Regardless of sector or where your business operates (online or offline or multi-channel), it is highly likely that the online world
will have some impact on business outcome – this is a pervasive and growing trend. Consumers don't make the distinction
between media and sales channels in the same way business reporting does. As such, data and analytics employed by
marketeers needs to mirror today's media environment, but also be flexible enough to mirror tomorrow's too. Online sources of
data (such as search query volume alongside click, impression, webvisit data, etc) are those that are most obviously missing in
legacy analytics. Employing these types of data in smart, and evolving analytics, alongside constant experimentation and
understanding of consumer opinion, forms the central pillar in effective and accountable marketing.
DATA TREATMENT/COLLECTION
PC City was keen to exploit the availability of data sources related to their customers’ purchase path. The plethora of
information goes well beyond just paid media. Other media and contacts deliver data that can be modelled to quantify media
synergy and dependencies.These in turn can translate into actions that really drive results.
Data sources
l Sales and website data collected directly from PC City's internal database and via third-party data providers
l Media information is provided by the media agency via detailed media plans, third-party media billing partners or
transaction platforms
l Search query volume and other online and social data streams can be accessed online (Google Insights for Search,
AdWords, Facebook ad performance, social listening services).
Data collected:
l Weekly or Daily Data for all variables
Downloaded from warc.com
4

5.
l Three years of historical data to capture structural and systemic trends
l Data by channel, product line and region
Data Landscaping
Once collected, data are cleansed and audited to create the modelling dataset. Before modelling even commences, the
database itself was visualised in a dashboard to enable a broad view of trends and correlations between driver variables and
outcomes.
ABOUT THE AUTHOR
Henry Eccles has responsibility for marketing and media insights at Google EMEA, where he helps large advertisers and
agencies make sense of and maximise return from their media investments.
heccles@google.com
© Copyright Warc 2011
Warc Ltd.
85 Newman Street, London, United Kingdom, W1T 3EX
Tel: +44 (0)20 7467 8100, Fax: +(0)20 7467 8101
www.warc.com
All rights reserved including database rights. This electronic file is for the personal use of authorised users based at the subscribing company's office location. It may not be reproduced, posted on intranets, extranets
or the internet, e-mailed, archived or shared electronically either within the purchaser’s organisation or externally without express written permission from Warc.
Downloaded from warc.com
5