“What is your expected return on investment for the next campaign?” This question is common and easy to ask, but it is also one of the most challenging to answer. Determining financial return on investment (ROI) within the context of a long sales cycle has largely been a failure. Becoming financially accountable is possible – but B2B marketing departments will need more advanced quantitative tools.
How should companies measure the financial value of marketing expenditure? For some ordinary company expenses, such as capital equipment, ROI is a common measure of value. Never before have there been more spreadsheets and dashboards available to slice and dice any facet of marketing. With information so available, it has never been more justifiable to treat marketing like an ordinary expense and ask for an ROI similar to the type that applies to the purchase of new equipment.
B2B Marketing is Not a Candy Machine
Yet attempts to measure meaningful financial ROI in a B2B marketing context using methods applied to capital expenditures have largely failed. They have failed because the assumptions underlying ROI calculations do not apply to the majority of B2B marketing situations.ROI calculations assume that a marketing program is a simple, unambiguous transaction. Such a transaction works like this: If I put quarters into a candy machine and push a button, a candy bar pops out. The investment (quarters) and the return (candy) are clearly and immediately connected.
Simple math can measure simple marketing. If I send mail piece X, I get 100 orders. If I send mail piece Y, I get 200 orders. Therefore, I should invest in Y. Because I know the cost of the mail programs (investment) and the resulting value (return), I can calculate ROI.If the only marketing activity conducted was the mail piece, then an ROI calculation would be simple arithmetic.
However, B2B marketing within the context of a long and complex sale is not simple. It is a multifaceted set of influences that is applied against quickly changing human buying behaviors. IDC research shows that it takes an average of 19 months to create a new B2B customer and includes multiple touches.
Compare our single direct-mail example with an actual marketing process that involves as many as 25 different media types over nearly two years. What role did ads play in getting the buyer to respond to either mail campaign? What about the sales call campaign that coincidentally occurred about the same time?
GUIDANCE: Don’t attempt to calculate simple B2B ROI. Simple ROI fails to measure B2B marketing value because B2B marketing value results from the accumulation of effects over time. Other types of success measures will be useful at measuring tactical programs, but financial ROI using data collected at this level cannot be accurate.
First Step: Get a handle on your “I” (Investment)
The fact that simple ROI fails to measure marketing value does not mean that businesses are forever doomed to wonder despondently about the value of their marketing investment. However, progress requires more data and more advanced quantitative tools.
One hurdle is the lack of basic financial data within marketing departments. IDC consistently sees that many tech marketing departments have a poor understanding of their “investment,” or “I,” much less an ability to then determine the “return,” or “R.”
GUIDANCE: Do start by measuring your “I” (investments). Track marketing investments carefully over time and seek to control these expenses. Each year, IDC produces a Marketing Performance Matrix based on our annual Tech Marketing Benchmark survey identifying best practitioners in marketing operational excellence. In collaboration with these best-in-class companies, we’ve identified the operational behavior and performance indicators most useful for investment control and other important practices. Tech marketers can participate (for free) in IDC’s this survey and receive a complimentary copy of the aggregate results to see how their company compares. Contact Joe Ferrantino (email@example.com) if you are interested in participating.
Next Step: Analytics are required to start down the “R” path
The candy machine example described previously is a model for an ultrasimple, “coin operated” buying process. A more complex process, such as B2B buying, has many more steps and requires a more sophisticated model. Although B2B buying is not neat and linear a well-thought-out, multi-stage, pipeline is a reasonable proxy.
By capturing lots of behavioral data at touch points along the way, marketers can describe buyers’ progress from step to step toward a purchase. Important metrics include conversion (how many buyers from early stages progress to the later stages?), velocity (how fast do buyers progress through the stages?), and volume (will the amount that buyers intend to buy satisfy the need for revenue?).
Clusters of data-producing programs then become the levers and knobs that a company uses to nudge buyers from stage to stage. B2B companies with data learn quickly about the interdependency of marketing and sales in nudging buyers through the pipeline. IDC research shows that the average large tech company invests $40K – $70K USD in marketing for each sales person. This ratio may change in the future as more companies exploit the leverage between the two functions.
Once an organization has collected enough data to model a reasonably accurate customer creation pipeline, business intelligence and analytics tools can be applied to tease out the contribution of various marketing program components to movement between pipeline stages. Combinations of different programs can be tested for their ability to increase conversion and speed velocity, thus creating a pipeline that delivers the required revenue at a continually lower cost.
GUIDANCE: Do invest in a data-driven, buyer-centric, customer creation process that links programs to a smarter pipeline, which in turn links to revenue. Simple ROI calculations do not work for complex processes. To start down the path towards a reasonable answer to the financial ROI question, invest in a more sophisticated customer creation process model, lots of data, and analytics..