Data integration maximise media roi by henry eccles admap, september 2011


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

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Data integration maximise media roi by henry eccles admap, september 2011

  1. 1.   Data integration: Maximise media ROI Henry Eccles Admap September 2011 
  2. 2.     Title: Data integration: Maximise media ROI   Author(s): Henry Eccles   Source: Admap   Issue: September 2011  Data integration: Maximise media ROI Henry Eccles Google EMEASpanish retailer PC Citys 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 spendHow to optimally allocate investments across media channels is the main goal of all business and is becoming increasinglycomplex. Traditionally, defining an optimal marketing mix has centred around a marketeers knowledge and experience in theirfield, reinforced with analytics focused on the linear economics between ‘paid’ media and business success. In todays digitaland data-rich world however, its 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 andprofitability of their store operation, but only a limited feel for how their various marketing levers, such as their website, pricingstrategy, 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 ofcustomised models for each of their main product lines (desktop, laptop and netbook). Each model incorporated data fromtraditional marketing activities (investment, GRPs, adstock) as well as rich digital data from consumer-initiated contacts andother outcomes believed to be an integral part of the business ecosystem. Broadly, the project broke down into four distinctphases: Downloaded from warc.com2   
  3. 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 vehicleand 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. Whatare the model and associated assumptions and how ‘rich’ is the data to fit the model. Todays digital environment givesmarketers access to this rich (highly variable) data. PC Citys approach was the integration of this data alongside soundassumptions of how media works today in driving both sales on and offline, as well as an understanding of the intermediateoutcomes of value in a consumer purchase path.As an example, search advertising was and is a growing part of PC Citys media mix. It was, however, not just this increasinglevel 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 thisinstance was the understanding that it works both as an independent variable of web visits and subsequent sales (both on andoffline), but also as a dependent variable of other marketing activities. It was therefore modelled accordingly andindependently 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 dataallows us to measure. A failure to understand the source driving consumer-initiated contacts will almost certainly cause adegree 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 anapproximation of consumer intent, but its limitations lie in that most advertisers have insufficient campaigns in terms of breadthand 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 (availablefrom Google Insights For Search) in its model as a signal of consumer interest. This data, in turn, was modelled alongside itsAdWords, website and sales data. The degree to which these three variables (query volume, search ad impressions, clicksand web visits) are serially correlated delivers an indication of how effectively PC City were in capturing online consumerinterest.It was from this analysis that PC City understood that they were underspent in paid search and that there was value to begained from a reallocated media mix. This same rationale and optimisation analysis was applied across all on and offline datasets on the foundation that feedback or interaction effects can exist between all sales channels and media (Figure 1).THE RESULTS Downloaded from warc.com3   
  4. 4.  The most striking finding for PC City was the role of the web and, specifically, its own website in driving offline in-storepurchase. It already knew that 4% of revenue came via online sales and a further 2% through online reservations, but theanalytics uncovered a further 10% of total sales coming by way of the ‘online to store’ or ROPO (research online purchaseoffline) effect - this a bigger sales driver than price promotion and TV adspend. Overall, it was found that marketing wasdriving 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, buthow the analytics could be predictive of future outcomes. Optimal recommendations of marketing resource across channelspointed 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 andconsumer 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 todays 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 worldwill have some impact on business outcome – this is a pervasive and growing trend. Consumers dont make the distinctionbetween media and sales channels in the same way business reporting does. As such, data and analytics employed bymarketeers needs to mirror todays media environment, but also be flexible enough to mirror tomorrows too. Online sources ofdata (such as search query volume alongside click, impression, webvisit data, etc) are those that are most obviously missing inlegacy analytics. Employing these types of data in smart, and evolving analytics, alongside constant experimentation andunderstanding of consumer opinion, forms the central pillar in effective and accountable marketing.DATA TREATMENT/COLLECTIONPC City was keen to exploit the availability of data sources related to their customers’ purchase path. The plethora ofinformation goes well beyond just paid media. Other media and contacts deliver data that can be modelled to quantify mediasynergy and dependencies.These in turn can translate into actions that really drive results.Data sources l Sales and website data collected directly from PC Citys 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.com4   
  5. 5.   l Three years of historical data to capture structural and systemic trends l Data by channel, product line and regionData LandscapingOnce collected, data are cleansed and audited to create the modelling dataset. Before modelling even commences, thedatabase itself was visualised in a dashboard to enable a broad view of trends and correlations between driver variables andoutcomes.ABOUT THE AUTHORHenry Eccles has responsibility for marketing and media insights at Google EMEA, where he helps large advertisers andagencies make sense of and maximise return from their media© Copyright Warc 2011Warc Ltd.85 Newman Street, London, United Kingdom, W1T 3EXTel: +44 (0)20 7467 8100, Fax: +(0)20 7467 8101www.warc.comAll rights reserved including database rights. This electronic file is for the personal use of authorised users based at the subscribing companys office location. It may not be reproduced, posted on intranets, extranetsor 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.com5