How channel mix impacts marketing effectiveness

Havas' Josh Tilley looks at the impact of the evolving media landscape as part of the IPA Advanced Certificate.

We are spotlighting some of the best essays from our MIPA qualifying courses and qualifications. Here, Havas' Josh Tilley (formerly of Red Brick Road) looks at the impact of the changing media landscape on marketing effectiveness as part of the IPA Advanced Certificate.

In order to explore the role and impact of channel mix on marketing effectiveness, the two elements must first be split. Firstly, the media landscape and channel mix, and secondly, an examination of marketing effectiveness, before examining the former’s impact on the latter.

We must first acknowledge the scale of recent change within media (choice and consumption) – the stagnation or decline of one-way ‘broadcast’ media, and development of two-way ‘digital’ media (Kapler, 2017) – with TV viewing in slight decline since 2010 (BARB, 2017) and an 8.6% year-on-year decline in daily newspaper circulations (Mediatel, 2018). In contrast, digital channels are growing rapidly, with UK adults spending 24 hours per week online in 2018 (OFCOM, 2018), up from 9.9 in 2005 (OFCOM, 2017), social media usage among internet users rising from 22% to 77% between 2007-2017 (OFCOM, 2018), and smartphone usage rising from 30% to 72% between 2010-2016 (OFCOM, 2017). To further complicate this, Binet and Field (2017) discuss the online-offline blur caused by VOD, web radio and newsbrands’ websites/apps. A way to simplify this is by categorising channels as ‘paid’, ‘earned’ and ‘owned’. ‘Paid’ corresponds to traditionally bought media (Warc, 2015), though would also include ‘paid search’, ‘owned’ includes packaging, websites and social media accounts, whilst ‘earned’, largely driven by digital innovation, equates to customer-driven media such as Twitter retweets, Facebook shares, or word-of-mouth (Blades, 2017). The relevance here is the challenge of balancing paid/earned/owned in developing a commercially effective media mix, amid this fragmented media landscape (Healy, 2017).

There are many learnings from Datamine 3 (Cox et al., 2011) that influence media mix, though two will be highlighted here. Firstly, TV’s enduring role within the media mix, with case studies featuring TV having a 75% effectiveness rate (for hard business effects) versus 53% for those without. Whilst TV viewing may be declining, and ad-free streaming services growing, TV advertising remains important. Secondly, as media choice increases, media mix becomes more complex (Warc, 2015), and coordinating disparate channels to convey a single, consistent message more important. But, there are diminishing returns in simply adding channels, with Datamine 3 (Cox et al., 2011) finding three ‘advertising media’ to be the most effective number of channels when driving hard business effects, but no obvious upper-limit when it comes to ‘all media’ (including PR, social and DM), when measuring soft business effects (like brand awareness). This raises an effectiveness question – in a race to include new media within channel mixes, are we losing sight on driving hard business metrics?

In terms of the impact of channel mix on effectiveness, there are many potential aspects to explore, though for brevity, only two will be explored here.

Firstly, whilst we’ve seen digital growth and increased media choice, there’s been a simultaneous decline in campaign effectiveness (based on ‘very large business effects’) since 2010, coinciding with a fall in brand-building effects and a steady rise in short-termism since 2006 (Binet & Field, 2017). Part of this short-termism is intertwined with digital-driven measurability and sales attribution, which can incorrectly lead to obsession with campaign ROI (that can be easily manipulated by reducing marketing spend), rather than long-term profitability (Thinkbox 2018).

In contrast to this short-termism shift, an example of using channel mix for immediate response and long-term brand-building comes from John Lewis (Binet, 2016). Beginning in 2009, they began investing in Christmas advertising campaigns, using different channels for different messaging – TV for emotional brand-building, press for rational messaging, and later, social media to ‘prime’ excitement and amplify ATL communications. The relationship between ‘paid’ and ‘earned’ is considered, using paid social video to tease/prime (and social as a platform for earned), an intense burst of TV to drive word-of-mouth (Golding, Weavers and Knight, 2012), VOD to capture light TV audiences (broadening reach), and music carefully chosen to maximise earned radio plays (Binet, 2016), combining to make paid media work significantly harder. This strategy dispels the myth that ‘earned’ media can easily achieve success without ‘paid’ media (Binet & Field, 2017). Progressively, ‘owned’ media was introduced, using in-store windows, apps and toys to extend communications, resulting in full emotional immersion into John Lewis, and driving long-term brand growth. So successful were they, the 2010-2011 campaigns achieved an ROMI of 800%, driving an incremental profit of £134.6m from 2009-2011 alone (Golding, Weavers and Knight, 2012), and featuring among the top 8% of effectiveness cases (Binet & Field, 2007).

Secondly, Binet and Field (2013) examine the optimum effectiveness ratio for brand-building and activation, categorising channels as being better at brand-building (sponsorship, OOH, brand TV) or activation (search, SMS, DRTV), with the former best for reaching broad audiences, and ‘activation’ better for targeted media and immediate sales. Though data-driven (Chan, 2016) targeting (Warc, 2017) is increasingly accurate, Binet and Field (2013) maintain their 60:40 ratio (brand-building:activation) remains relevant. An example from Mattesson’s examines this activation vs. brand-building approach.

In 2011-2012 Mattesson’s were in decline (Chicourel & Poskett, 2016), outspent by competitors, without a role, and traditional communications proving ineffective. The response was redistribution of TV budget into a targeted ‘gaming’ audience, with a social-led campaign, competition, and a gaming-led content partnership. This proved successful, with 120m paid Facebook impressions, millions of YouTube views and a respectable ROI, but econometrics found soon after the campaign finished, there was no lasting sales effect (Binet & Field, 2017), though this may say more about the unsuitability of using econometrics to measure digital media (Warc, 2018). However, in their 2014-2015 campaign, whilst ‘gaming’ was still central, the idea was extended into TV and onto packaging, achieving a long-term ROI of £4.22 (Chicourel & Poskett, 2016), reversing sales decline, and having an effect months beyond the campaign period (Binet & Field, 2017). This provides evidence for Binet and Field’s (2013) 60:40 rule, as whilst activation drove short-term success, only when brand-building media was included was there a long-term sales effect.

To conclude, though we’re beginning to see more effective integration, there’s room for improvement, with short-term metrics sometimes prioritised over long-term business effects (Thinkbox 2018). Whilst digital media provides a “fire-hose of real-time…data” (Symonds, 2014), failing to consider long-term effects can mean sales become tied to communications, as shown by Mattesson’s first phase (Chicourel & Poskett, 2016), ultimately punishing effectiveness. Equally, whilst broadcast media may be ‘faltering’, the effects of combining TV and digital media are strong (Binet & Field 2017). A potential solution is to approach campaigns with media neutrality (Elms, 2017) – not swayed by digital novelty or ‘old-media’ prejudice – whilst Symonds (2014) advocates focusing on “delivering increased relevance and value for…consumers”, using the power of digital data and customer information to “create seamless brand experiences”.

Josh Tilley is a Strategist at Havas. This essay earned him a Distinction for the IPA Advanced Certificate during his time as a Planner at Red Brick Road.

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Last updated 01 May 2024