2. 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.
3. 2
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
4. 3
• 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:
5. 4
• 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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Figure 2
Page 8
Defining engagement metrics in emerging media
Examplegeneric digital metricsDimension
ADVOCACY
DISCUSS
PARTICIPATION
INTERACTION
RESPONSE
EXPOSURE
# Blogsdiscussing topic, Rich media response (eg
video response), % active members
Opinions, External blog comments,Trackbacks, Wiki
participation
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
Examplegeneric digital metricsDimension
ADVOCACY
DISCUSS
PARTICIPATION
INTERACTION
RESPONSE
EXPOSURE
# Blogsdiscussing topic, Rich media response (eg
video response), % active members
Opinions, External blog comments,Trackbacks, Wiki
participation
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.
