One of our clients, a consumer electronics giant, had long gauged its advertising impact one medium at a time. As most businesses still do, it measured how its TV, print, radio and online ads each functioned independently to drive sales.
The company hadn’t grasped the notion that ads increasingly interact. For instance, a TV spot can prompt a Google search that leads to a click-through on a display ad that, ultimately, ends in a sale.
To tease apart how its ads work in concert across media and sales channels, our client recently adopted new, sophisticated data-analytics techniques. The analyses revealed, for example, that TV ate up 85% of the budget in one new-product campaign, whereas YouTube ads – a 6% slice of the budget – were nearly twice as effective at prompting online searches that led to purchases. And search ads, at 4% of the company’s total advertising budget, generated 25% of sales. Armed with those findings and the latest predictive analytics, the company reallocated its ad dollars, realising a 9% lift in sales without spending a penny more on advertising.
That sort of insight represents the holy grail in marketing – knowing precisely how all the moving parts of a campaign collectively drive sales and what happens when you adjust them. Until recently, the picture was fuzzy at best. Media-mix modelling, introduced in the early 1980s, helped marketers link scanner data with advertising and decide how to allocate marketing resources. For about 20 years, everyone gorged on this low-hanging fruit, until the advent of digital marketing in the late 1990s. With the ability to monitor every mouse click, measuring the cause-and-effect relationship between advertising and purchasing became somewhat easier. Marketers started tracking a consumer’s most recent action online and attributing a purchase behaviour to it.
Combined with a handful of time-honoured measurement techniques – consumer surveys, focus groups, last-click attribution – such outmoded methods have lulled many marketers into complacency. They mistakenly think they have a handle on how their advertising affects behaviour and drives revenue. But that approach treats advertising touch points – in-store and online display ads, TV, radio, direct mail – as if each works in isolation. Making matters worse, different teams, agencies and media buyers use different methods of measurement as they compete for the same resources. This still-common practice, what we call swim-lane measurement, explains why marketers often misattribute specific outcomes to their marketing activities and why finance tends to doubt the value of marketing.
Marketers who stick with traditional analytics 1.0 measurement approaches do so at their peril. Those methods, which look backward a few times a year to correlate sales with a few dozen variables, are dangerously outdated. Many of the world’s biggest multinationals are now deploying analytics 2.0, a set of capabilities that can chew through terabytes of data and hundreds of variables in real time. It allows these companies to create an ultra-high-definition picture of their marketing performance, run scenarios and change ad strategies on the fly. Enabled by recent exponential leaps in computing power, cloud-based analytics and cheap data storage, these predictive tools measure the interaction of advertising across media and sales channels, and they identify how exogenous variables (including the broader economy, competitive offerings and even the weather) affect ad performance. The resulting analyses reveal what really works.
With these data-driven insights, companies can often maintain their existing budgets yet achieve improvements of 10 to 30% in marketing performance.
The move to 2.0
Analytics 2.0 involves three broad activities: attribution, the process of quantifying the contribution of each element of advertising; optimisation, or “war gaming” by using predictive analytics tools to run scenarios for business planning; and allocation, the real-time redistribution of resources across marketing activities according to optimisation scenarios.
To determine how your advertising activities interact to drive purchases, start by gathering data. Many companies we’ve worked with claim at first that they lack the required data in-house. That is almost always not the case. Companies are awash in data, albeit dispersed and often hidden. Relevant data typically exist within sales, finance and other functions outside marketing.
Knowing what to focus on is critical. To accurately model their businesses, companies must collect data across five broad categories: market conditions, competitive activities, marketing actions, consumer response and business outcomes.
With detailed data that parse product sales and advertising metrics by medium and location, sophisticated analytics can reveal the impact of marketing activities across swim lanes – for example, between television and social media.
Once a marketer has quantified the relative contribution of each component of its marketing activities and the influence of important exogenous factors, war gaming is the next step. It involves using predictive-analytics tools to run scenarios for business planning.
Working with the vast quantities of data collected and analysed through the attribution process, you can assign an “elasticity” to every business driver you’ve measured, from TV advertising to fuel prices. (Elasticity is the ratio of the percentage change in one variable to the percentage change in another.) Knowing the elasticities of your business drivers helps you predict how specific changes you make will influence particular outcomes.
War gaming uses the elasticities of your business drivers to run hundreds or thousands of scenarios within minutes. In a typical war-gaming process, team members define marketing goals, often across multiple products and markets. Crunching the vast database of driver elasticities, optimisation software generates a set of most-likely scenarios along with marketing recommendations to achieve them.
Gone are the days of setting a marketing plan and letting it run its course. As technology, media companies and media buyers continue to remove friction from the process, advertising has become easier to transact, place, measure and expand or kill. Allocation involves putting the results of your attribution and war-gaming efforts into the market, measuring outcomes, validating models (that is, running in-market experiments to confirm the findings of an analysis), and making course corrections.
Five steps to implementation
- Embrace analytics 2.0 as an organisation-wide effort that must be championed by a C-level executive sponsor.
- Assign an analytics-minded director or manager to become the point person for the effort.
- Armed with a prioritised list of questions you seek to answer, conduct an inventory of data throughout the organisation.
- Start small with proofs of concept involving a particular line of business, geography or product group. Build limited-scope models that aim to achieve early wins.
- Test aggressively and feed the results back into the model.
Marketing is rapidly becoming a war of knowledge, insight and asymmetric advantage gained through analytics 2.0. Companies that don’t adopt next-generation analytics will be overtaken by those that do.
Wes Nichols is a co-founder and the CEO of MarketShare, a global predictive-analytics company headquartered in Los Angeles. Harvard Business Review, © 2012 Harvard Business School Publishing Corp.