Global Data & Analytics

The ROI of brand advertising in B2BBy Dimitri MaexManaging Director Marketing EffectivenessOgilvy New York

Ogilvy & Mather

Introduction When a complex multi-million dollar deal gets closed in a typical B2B environment, it is usually the result of a complex interplay between various sales and marketing efforts. Optimizing the mix of these efforts is the ultimate goal of every B2B marketer. In order to do this they first need to understand and quantify the contribution of every sales and marketing effort to the bottom line. In B2B that can be a very tough job. For some efforts this will be relatively straightforward. The sales force will have worked on the lead and can very easily be held accountable for a share (if not all) of the revenue associated with it. The initial lead might have been generated and then nurtured through addressable demand generation tactics such as emails or DM. The contribution of these tactics to the pipeline can be usually be traced directly. Or perhaps the lead was generated through direct response TV or Print. Unique URL’s or phone numbers might be able to capture most of the revenue contribution here. But when it comes to brand advertising or any other activity at the top of the sales and marketing funnel, it’s a different story. That is a problem in a world where marketers are increasingly being asked to demonstrate the impact of all their efforts on the bottom line. Failure to do so for brand advertising is likely to move dollars away from brand TV, radio and print in favor of addressable demand generation marketing activities or investments in the sales-force. This may very well be a good decision. But to make it solely because it is harder to quantify the contribution of brand advertising to the bottom line would be a mistake. The fact that addressable media and sales force activity are more measurable doesn’t make them more effective. This paper will put forward a practical approach to tackling this problem. We do not offer the ultimate answer to this complex question. Nor will we reveal an all explaining statistical formula that will tell you exactly what the optimal levels of marketing and sales investments are to generate maximum revenue. Instead we will first expose some of the difficulties of directly linking brand advertising activity to sales in a B2B. We will then summarize some of our experience of working with B2B companies that have tried to prove brand advertising’s impact beyond the traditional market research metrics like awareness and consideration. We will finally put forward a pragmatic approach to getting closer to the ultimate answer by using data points from the digital world. While this approach may not lead to a true optimization engine that will tell us exactly how to spend our marketing dollars, it will hopefully suggest a measurement framework that will assess brand advertising’s performance by metrics that are more closely linked to revenue and that are also easily available.

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B2B challenges Most B2C companies quantify the impact of brand advertising on sales by looking at weekly sales and media impressions. If the brand advertising was effective they would probably see an increase in sales during or right after periods where they would run the communications. They could even use econometric modeling to really quantify the sales increases attributable to individual media. This approach tends to be much harder in B2B for the following reasons :

• Influence of decision making units : purchasing decisions in B2B are usually made by a number of people in a decision making unit (DMU). Finding a correlation between an individual’s marketing exposure and the purchase decision made by the DMU the individual belongs to is hard.

• Longer Sales Cycles : Sales cycles in B2B tend to be longer. This means there are bigger time lags between marketing messages and the response they could generate as measured by pipeline metrics or an actual purchase. Months can go between these two events, which makes it especially hard to disentangle the cause and effect over time.

• Multiple touches : Because of the longer sales cycles decision makers tend to get exposed to a high volume of different marketing messages before they make a purchase. This often adds to the complexity.

• Impact of a sales-force : The sales-force tends to plays such a big role in the closing of deals that it often becomes impossible to observe a direct impact of marketing on B2B revenue.

• Fewer bigger deals : B2B purchases tend to be bigger and less frequent. This can make weekly sales data for a B2B company very volatile.

All these factors make it incredibly hard to measure the impact of general brand advertising on revenue in many B2B environments. So what can we do to demonstrate effectiveness? Intermediate Metrics One option is to revert to intermediate metrics such as brand awareness and consideration. While it is important to understand the impact of brand advertising on these metrics it is not enough. As mentioned earlier, marketers are under increased pressure to demonstrate the contribution of their activities to the bottom line. One way to do this is to find intermediate metrics that are better indicators of financial performance than traditional awareness and consideration metrics. Quantitative research can prove that certain metrics are correlated to purchase intent. “Openness to taking a sales call” for example is likely to be correlated with revenue. It can also probably be more easily directly influenced by non addressable media. This would make it a much more powerful intermediate metric than awareness for example. If multiple metrics like this are found one could create one composite metric. IBM is one example of a B2B company that used this approach. They created what they call the “Favorable Selling Environment” Index (FSE). The FSE index is made up of an IT decision maker’s likelihood to :

• Recommend IBM to a colleague

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• Seek out more information about IBM’s products and services (e.g., visit the company’s Web site, visit its booth at conferences or trade shows, call the company, request product or service information, or click on an online ad)

• Meet or take a call with IBM’s sales representatives • Identify yourself to IBM as being interested in its products or services • Ask colleagues about their opinions or experiences with IBM • Request a bid or proposal from IBM.

IBM proved through research that the FSE index was highly correlated with purchase intent. This proof allowed them to get buy-in from the finance community and the sales organization that this metric was a good predictor of financial performance. They then put in place a tracking survey that gathered the information needed for calculating the FSE index. This allowed them to monitor the performance of their brand advertising campaigns. It also gave them the time series data that allowed them to use econometric models for optimization. There are a number of advantages to this approach :

• It works : It helps the B2B marketer prove the financial contribution of non addressable media

• The CFO gets it : It’s a common sense approach that is easily explained to the finance community.

• It’s simple : It can potentially provide 1 metric that can used for optimization. • It’s easy to implement : Clients often already have the research in place to do the analysis

to identify the metric(s)

One of the downsides is that these metrics need to be gathered through market research. Gathering this data frequently enough so that it can be used for optimization would be expensive. This is where digital intermediate metrics can be useful. Digital Intermediate Metrics The breadth and depth of digital metrics is expanding every day through innovations in online tracking technology. They are easy and inexpensive to gather and they are available at whatever time interval needed. If crafted creatively, they can also be indicators of activities that are closer to the point of purchase. Their availability over time also makes them perfect for multivariate time series analysis. Here is one example of an analysis we did for one of our B2B clients that illustrates this. Brand advertising’s impact on visits to site homepage We performed an in depth analysis for one of our B2B clients with the objective to quantify the effect brand advertising (TV, print, online) has on visits to the clients homepage. We performed multivariate time series analysis to assess the impact of multiple factors on homepage visits over time:

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• TV GRPS1 – the overall level of TV activity over time • TV with URL – an indicator of whether the TV spot included a URL or not • Print – the overall print impressions during a period of time • Search – the number of search impressions during a period of time • Online Advertising - the overall banner impressions during a period of time • Holidays – a flag that indicates whether the week included a public holiday • External events – exogenous events were captured by the client’s stock price deviations

from its average2. Figure 1 shows the actual visits to the homepage on the red line, compared to number of visits as predicted by the model. The two lines follow each other closely, clearly demonstrating the performance of the model which had an R2 of 89%. This means that 89% of the variation in week to week visits to the homepage can be explained by the variables described above. A closer look at the model reveals the following insights:

• TV had a positive impact: for every 10 TV GRP’s per week we drove an estimated incremental 1,720 weekly visits to the homepage.

• Including a URL in the TV ad had a positive impact: it generated on average an incremental 48k visits per week to the homepage.

• Print did not show up in the model: This was mainly due to the fact that the print schedule had very little variation over time. This makes it impossible to disentangle print’s influence from the other factors.

• Online advertising did not have a significant impact: it did not generate significant incremental visits to the homepage which is to be expected as it drives traffic to the campaign landing page.

• Weeks with significant holidays showed on average 77k less visits to the homepage. • For every 1 point stock price deviation (pos or neg) from the average we see an

incremental 3,203 visits to the homepage. This variable was included to capture exogenous variables.

1 Gross Rating Points : A measure of the advertising weight delivered by a vehicle or vehicles within a given time period (reach times average frequency). 2 The hypothesis was that if external events such as quarterly revenue announcements occurred, both the stock price would move and the number of visits to the homepage would increase. By incorporating absolute stock price fluctuations into the models we could test this hypothesis and control partially for external events.

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Figure 1

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WebHits Predicted Value

Predicting visits to homepage

RSqd Fit = 89%

This exercise clearly demonstrated the impact of TV advertising on the volume of traffic to the homepage. One could debate the usefulness of total visits to the homepage as a metric to gauge advertising’s performance. Driving traffic was not necessarily one of the objectives of the TV advertising and the actual value of visits to the homepage is not clear (i.e. what is the quality of the visits and do these visits ultimately lead to revenue). This is where digital engagement metrics could help. Digital Engagement Metrics “Engagement” is a phrase many in the industry have coined to define how customers are involved and participate with digital touch points in a web 2.0 environment. New internet technology and content architecture have finally made the internet the two way or conversational medium it always promised to be, resulting in digital experiences that extend beyond the confines of the “traditional” website. Digital media are being affected by external digital influences such as blogs. Digital analytics is starting to come of age as well. Because of new tracking technology being developed constantly, the internet is starting to deliver on a second promise it always had: everything is measurable. The combination of richer digital customer experiences and engagement on the one hand and increased sophistication of digital tracking mechanisms on the other has given birth to a myriad of digital engagement metrics. We usually categorize these metrics in 6 engagement levels:

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Defining engagement metrics in emerging media

Example generic digital metricsDimension

ADVOCACY

DISCUSS

PARTICIPATION

INTERACTION

RESPONSE

EXPOSURE

# Blogsdiscussing topic, Rich media response (eg video response), % active members

Opinions, External blog comments, Trackbacks, Wikiparticipation

Forum members, Votes cast, Survey response, Blog comments, Conversation depth

Downloads, New subscriptions, Website actions completed

Banners Clicks, Organic search referrals, Percentage viewed, Completed videos, Geo-response (eg # countries responding)

Display media impressions/reach, Organic searches, Video viewings, Page views / Visits, Podcast/ Vidcast/ Blog (RSS) subscriptions

Example generic digital metricsDimension

ADVOCACY

DISCUSS

PARTICIPATION

INTERACTION

RESPONSE

EXPOSURE

# Blogsdiscussing topic, Rich media response (eg video response), % active members

Opinions, External blog comments, Trackbacks, Wikiparticipation

Forum members, Votes cast, Survey response, Blog comments, Conversation depth

Downloads, New subscriptions, Website actions completed

Banners Clicks, Organic search referrals, Percentage viewed, Completed videos, Geo-response (eg # countries responding)

Display media impressions/reach, Organic searches, Video viewings, Page views / Visits, Podcast/ Vidcast/ Blog (RSS) subscriptions

DECREASING AUDIENCE SIZE

INCREASING AUDIENCE VALUE

If interpreted correctly, these metrics can provide deep insight into how customers experience the brand online. We believe that they can also be a very powerful tool for understanding the impact of all elements of the communications mix on revenue. They give us invaluable observation points of how customers are moving through the selling process, all the way from awareness to the actual purchase. If crafted creatively they can have a similar meaning as the more traditional intermediate metrics such as the FSE index described above. But they would be a lot cheaper and easier to collect which could make them available at a very high frequency – ideal for optimization. Conclusion In complex B2B environments it is often very difficult to quantify the direct contribution of brand advertising to the bottom line through techniques that are often used in B2C. However, B2B companies can correlate brand advertising to intermediate metrics that are closely related to revenue. Digital engagement metrics can be a very rich and cost efficient data source for this.