Conversation impact social media measurement by irfan kamal and john bell

of 8Creative Commons with Attribution license. Attribute: Ogilvy 360 Digital Influence.

Conversation Impact™: Ogilvy’s Simple, Results-Driven Social Media Measurement Model for Marketers

Track the few metrics that really matter.

With two-thirds of the world’s Internet population

now visiting a blog or social networking site, driving

results through social media has become

an important component of the marketing strategy.

To help guide brands on social media spending

decisions, Ogilvy’s global social media marketing

group, 360° Digital Influence, has developed

and introduced a new business objective-driven

model that provides a quantitative measurement

framework for [social] media effectiveness—

Conversation Impact.

Irfan A. KamalVice President/Digital Influence Strategist360° Digital InfluenceOgilvy Public Relations [email protected]

Contact:

Irfan A. KamalVice President/Digital Influence Strategist

360° Digital Influence

Ogilvy Public Relations Worldwide

Tel: 202 729 [email protected]

John H. Bell Managing Director/Executive Creative Director360° Digital InfluenceOgilvy Public Relations [email protected]

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Conversation Impact™ (continued)

Creative Commons with Attribution license. Attribute: Ogilvy 360 Digital Influence.

In developing Conversation Impact, we had three key goals in mind:

1. The approach must allow for cross-channel performance comparison, specifically including the social media “channel”

2. The metrics must include actionable data for in-market campaign optimization

3. The model must be simple, objective-driven and cost-effective enough to use for every campaign, social media and “360”/multi-channel

In addition to introducing the model below, the following sections walk through some specific considerations and objectives we worked through.

Measure cross-channel. De-emphasize metrics that don’t allow apples-to-apples comparisons.

Current approaches to advertising focus on measurement of such items as ad recall, ad reach, ad frequency, in-category brand aided/unaided recall (for evaluating awareness/consideration), intent to purchase and net promoter surveys (for evaluating preference); and—for measurable media such as online ads—action/conversion rates (for evaluating action or conversion).

Traditional metrics are not readily applicable to the analysis of social media—but are still being used. Reach/exposure (the number of people exposed to the message) is often used as a metric related to brand awareness, positioning and preference campaigns. However, word of mouth WOM is consistently trusted more than other forms of communication/marketing

1, so direct

CPM or impression-based comparisons are not useful.

Another complicating factor is that simply applying an adjustment factor to a WOM mention is still not useful because of the varying

1 eMarketer Bridge Ratings and University of Massachusetts 2007

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influence levels of the conveyor of the message. That is, people trust messages differently based on context (which includes factors such as the person conveying message, tonality of message and specific content of the message).

Finally, simply reporting “activity” metrics like page impressions, interactions and time does not help marketers determine whether or not a campaign was successful in driving their ultimate marketing or communication goals. Even measures of “engagement” cannot connect activity to core marketing goals.

Conversation Impact simplifies and standardizes measurement in a way that is readily recognizable to marketers, while accommodating both conventional and new metrics and data that account for these differences in social media.

Provide actionable measurement that focuses on key goals.There is rich data now available on a continuous basis, including daily data from the semantic analysis of the millions of conversations in social media. We integrate these analytics into the model to help drive optimization.

As an example, for a brand positioning campaign, we can evaluate preference and action in ways that help us understand which social media influencers are adopting which types of messaging and in which channels. We look at what people voluntarily say and do across the social Web. We can use this data to help guide ongoing creative and spending decisions.

Use a model that’s both strategic and simple.We organized our model directly around 3 main goal categories that build off years of “marketing/communications funnel” research into the best way to drive action:

Awareness & Positioning•Preference•Action•

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Table 1 presents the key representative measures within each of these categories.

Some of these metrics are based on data from social media moni-toring software; other metrics are obtained from server logs, Google and other analytics data, and surveys.

Here are examples of data sourcing and definitions of key metrics shown above:

Awareness/Reach/PositioningShare of total voice within category = volume of mentions for •brand / total volume of discussion in category3

2 Depending on volume and the prevalence of negative discussion, we may look at share of net positive voice, where net positive voice = brand positive mentions – brand negative mentions 3 The denominator may also be modified to include only the brand plus specific competitors

Table 1: Conversation Impact Model with Representative Metrics

Metric / Funnel Goal > Awareness Consideration Preference Action Loyalty

Reach/Positioning

UMV – blogs, site, microsite, applications, other, total #/% •change

Volume of online conversation, #/% change, Cost per Online •Conversation Generated (CPICG)

Share of voice in category (=Volume for brand/volume for •category). #/% change, Cost per Increase in Share of Voice (CPISV)

Search visibility (for relevant keywords)•

Preference

Sentiment index of online conversation (% positive–% nega-•tive), #/pts change, people reached vs. all, Cost Per Increase in Sentiment Index (CPISI)

Share of positive voice in category (= brand positive mentions •/ category positive mentions), # / % change, people reached vs. all, Cost per Increase in Share of Positive Sentiment (CPISP)2

Relative net promoter score (NPS) in category (=brand NPS / •category NPS), #/% change, people reached vs. all, Cost per Point Increase in NPS (CPINP)

Action

Registration: RPA, CPA, $, #•

Sale: RPA, CPA, $, #•

Advocacy: RPA, CPA, $, #•

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Calculated via social media monitoring/listening software or •through a direct consumer survey Cost per point increase in above metric•

PreferenceShare of positive voice within category = volume of positive-•sentiment mentions for brand/total volume of positive-sentiment mentions in category Can also be compared to / calculated as share of net positive •voice, which = volume of (positive – negative) mentions for brand / total volume of (positive – negative) mentions in categoryCalculated via social media monitoring/listening software or •through a direct consumer surveyCost per point increase in above metric•

ActionCampaign- or influencer-attributable actions•Calculated using tracking analytics or through Ogilvy’s social •media activation platform technology

Note that the measures shown are representative, not comprehensive—the key focus of the model is to use categories and metrics that provide simple, useful data in ways that provide for ease of comparability and analysis.

Evaluate campaign impact on influencers, consumers or both.

We used the model to evaluate two example brand campaigns. We selected these two campaigns to demonstrate the flexibility of the model in measuring impact on both influencers and consumers. We define an “influencer”—someone who is effective at broadly distributing a message or driving action—based on a number of factors, including a person’s connectedness, reach and ability to engage and drive results around a specific target audience and discussion topic. For the purposes of this discussion, “consumer” represents traditional end users/target audience members.

We employed our social media listening post and survey-based data collection methods for these campaigns.

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Campaign 1: Consumer Impact—Evaluate Impact based of a Social Media Community Program on Consumer Aided Awareness and Preference

Campaign 1’s social media program analyzed consisted of a blog community-based program. We evaluated pre-campaign and in-terim measures based on a survey instrument. Measures evaluated included aided awareness and purchase intent.

Campaign 2: Influencer Impact—Evaluated Impact on Influencer Preference for the Brand based on a Multi-Channel Campaign Including Social Media

Campaign 2’s social media program included social media compo-nents and a multi-channel traditional and online advertising campaign.

We used a software monitoring product to track and categorize the tonality of social media mentions around the brand. This product’s algorithm classifies social media discussions based on a trainable, semantic, natural language-based categorization algorithm and has a high degree of consistency and reliability in assigning social media mention sentiment and topic relevance4.

4 It should be noted that our team’s approach is software-independent. We set up Social Media Listening Posts™ with underlying software that is the most appropriate for the specific client engagement.

Figure 1: Consumer Impact—Awareness and Preference

Social Media Campaign Impact: Consumersn Pre n Post

20%

67%

6%

48%

10%

48%

Aided Awareness Intent to Purchase (9 or 10) Positive Opinion (9 or 10)

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Our focus for this measurement program was to evaluate the impact on influencers driving brand positioning and preference in social media; specifically, we evaluated the number of positive mentions as a share of total positive mentions within the brand’s competitive set. There were five competitors identified to be part of the competitive set.

For Campaign 2, we found an increase in influencer preference in social media mentions—as measured by relative share of positive voice in social media—of 1.5 percentage points during the first 3 months of the campaign. This represented a 9.2% increase in pref-erence among those posting in social media about the brand.

Figure 2 shows the monthly trend in preference.

Use the model today.

In our early uses of the model, we’re finding that Preference and Positioning are two key areas in which social media can deliver particularly efficiently—and we’re now implementing new Action-based campaigns to further expand the range of social media goals.

Figure 2: Influencer Impact—Share of Positive Voice in Social Media

Social Media Campaign Impact: InfluencersShare of Positive Voice in Social Media

Dec-08

15.0%

16.0%

17.0%

18.0%

19.0%

16.2%

17.1%

17.7%18.2%

Jan-09Pre-Launch Campaign Launch

Feb-09 Mar-09

4-month Trend

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We believe the model represents a useful step forward in social media impact measurement, primarily due to its focus on tracking metrics with comparability across different types of advertising and communications.

As social media increasingly becomes a standard component of both advertising and communications campaigns, this type of simple, cross-channel comparative framework will become more useful in answering media allocation questions and helping marketers decide which social media efforts are worthy of scaling further.

Conversation impact social media measurement by irfan kamal and john bell

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