Monday, 16 January 2012

Are you ready for Real Time Planning?

This is a reproduction of an article I've written for this month's Admap. They've chosen to title it 'Track the data on the dashboard', which I think rather misses the point but there you go. On Wallpapering Fog, I choose the headlines.

Real-time planning is a tactical tool that, through analysis of customer behavioural data, enables the short-term refinement of communications strategy, explains Neil Charles of MediaCom.

Real-time planning is one of those marketing terms that has a danger of meaning different things to different people, so I'd like to start off with a brief definition. For me, real-time planning means adapting marketing schedules on the fly, in reaction to new data about how customers behave.

The challenge that this type of adaptable marketing presents is to process new data and then react quickly enough, to take advantage of opportunities as they are identified. However, too often, marketers expect data on its own to be enough and that deep insights will reveal themselves if only we can bring different data sources together. Analysts have known for a long time that this is rarely the case, but large quantities of consumer data are seductive. Surely we could build a more efficient, more flexible media schedule if we had more up- to-date tracking of consumer behaviour?

Inevitably, the data that has provoked this new marketing philosophy flows from the web. We have faster access to more granular data than ever before, both in terms of marketing response through clicks and traffic tracking, and also the ability to ask questions of large online research panels cheaply, and to see the results in a very short period of time.

In practical terms, the web will largely be the focus for the outputs from real-time planning too. Traditional media - where the creative process and buying deadlines are longer-lend themselves much less readily to the type of quick schedule changes, which allow us to take advantage of new data. This online focus should put real-time planning in context for marketers as an exciting new possibility, but one which must never be allowed to compromise an overall campaign. The Internet Advertising Bureau and PricewaterhouseCoopers put UK internet spend at £4 billion in 2010, accounting for 25% of all advertising spend. So while we may have the ability to monitor consumer behaviour (on the web at least) in almost real-time, only a part of the marketing budget is as agile as the response data that we can monitor. Of course, TV or press schedules can be adjusted, but once a commitment to TV has been made, barring disaster, the ads will run largely as planned.

Crucially, most ads should run largely as planned. We often preach the benefits of consistency in advertising and of seeing a brand campaign through, for its full benefits to be felt. Real-time planning doesn't replace the normal planning process, but is about tactical adjustments to a campaign that has been well planned in advance. If our understanding of new data is allowed to constantly re-shape a brand's proposition then we risk compromising our ability to put across a consistent message to consumers.

So, with real-time planning in context as a tactical, rather than strategic tool, and one that is based on very recent data about our customers, what do we need to do to make it work?

It is easy to generate and to track extremely large volumes of customer data. Over the past few years, dashboard software has become cheap and capable, and for a small IT investment, marketers can easily bring together their sales information every week, their own brands' and competitors' advertising spends, response data from off-line direct marketing channels and web tracking from a count of homepage visitors, right down to the number of clicks on individual Google keywords. We can also incorporate brand mentions and sentiment from social networks, track PR coverage both online and offline and conduct quick consumer research polls.

Collecting this data and visualising it, in the hope that it will provide insight and lead to greater marketing efficiencies usually results in disappointment. Large volumes of data, without analysis, are more of a hindrance than a help and, unfortunately, analytical insights very rarely jump off the page from a single chart.

Even where a relationship is obvious - such as when the number of brand term searches is charted against TV investment - what do we do with this information? It's not enough to know that TV is driving additional customers to search for us on Google. We need to know whether this means we should increase the TV budget, attempt to convert more of the online interest that TV is shown to be generating - both, or possibly neither. After all, the current schedule appears to be working!

Rather than tracking large volumes of data and hoping to generate insights from them that will lead to more efficient marketing, the data that we choose to track should flow from analysis work that has already been completed.We need analysts to identify from the vast quantity of available information, variables which are useful, show how they can be used and then to hand that information to marketers.

A recent client example concerned a business which had no concrete data on overall sales volumes in its market, but many variables that might indicate whether they were rising or falling. Sales in the client's business were rising and they wanted to know whether - as some believed internally - this was bucking the market trend, or following it. The answer would have significant implications for advertising, since if the overall market wasn't getting stronger, then the most likely candidate to have caused the extra sales was a recent increase in marketing spend.

Large volumes of data were available that might provide insight, from a set of total market sales estimates that may or may not have been reliable, through to a number of Google searches for various brand and product terms and government economic data on the health of related sectors. The data contradicted each other and tracking alone raised many more questions than it answered.

A long-term econometric study into the drivers of sales had recently been completed, which identified a few key Google search terms that accurately mirrored market trends. This prior analysis flagged up data that was worth tracking and which could answer the question: No, marketing response didn't appear to have changed, and yes, increasing sales were being led by a market recovery.

The key point here is that the data we track to aid our marketing efforts, and which we aim to use to refine campaigns on the fly, should already have an identified purpose at the point when we decide to track it. Data that we do not yet understand in detail doesn't allow us to plan in real-time, it raises questions, which first need to be answered. Answering those questions is an analysis process that can take from a few weeks, to several months.

Together with data, which is already well understood, a second ingredient is needed for real-time planning to work. We need to know beforehand, what our likely reaction will be to a change in the data.

Marketing dashboards, metrics and tracking should be like the petrol gauges or the speedometer on a car. When they change, we already know why and so we already know what to do about it. When the petrol gauge gets too low, we stop and fill up, to avoid an embarrassing call for a tow from the side of the road.

A lot of information about your car isn't displayed on the dashboard. Not because it isn't useful at all, but because it isn't useful minute-by-minute and would be a distraction from driving. This sort of information - on engine efficiency for example - is checked annually when the car is serviced. Marketing analysis should work the same way, meaning that we track what we already understand and can respond to, and ask mechanics (our planners and analysts) to react to more complex data once or twice a year. What the analysts discover might increase the scope of real-time planning as different data becomes well understood. To stretch the car dashboard analogy, we might gain new warning lights on the dashboard, but we are unlikely to start visualising large quantities of new data.

Without prior analysis, there is a sub-set of data that is always useful and, realistically, this is where a lot of brands already do 'real-time planning', whether it is labelled as that or not. Based on direct response data from clicks or phone calls, under-performing press insertions, search keywords and display placements can be pruned from a schedule in real-time without any need for further analysis. Their budget will be allocated to ads with a better response rate, and so all we need to know is that there is a better ad where we could be spending the money instead. It doesn't matter what is the true return on investment to a display ad - only that we can move budget from an under-performing ad to a stronger one that generates more clicks for the same money.

I should point out here that I'm absolutely not arguing against collecting marketing data. We have incredible quantities of information at our disposal to track and better understand consumers and we should keep them, because we don't always know what will be useful until later. This article is about reacting to that data in real-time and day-to-day, those volumes of data become a hindrance rather than a help. Once we focus only on the data that we genuinely understand, a lot of available data - from follower counts to web traffic - becomes surplus to requirements until somebody can work out why it's useful and what it means when it changes.

Even a measure of total sales or footfall to a store, is of dubious value to a marketer who doesn't know what impact the brand's marketing activity will have on the metric. A drop in sales presents two immediate possibilities - spend more on marketing in order to restore sales to where they were previously, or cut marketing in response to a worsening business environment. The data only becomes useful and real-time planning becomes possible, if econometric models or other in-depth response analyses are already in place. Then it is possible to estimate what marketing can achieve, given the data that we're tracking and to decide on the best course of action.

In summary, I would argue that real-time planning is a tactical, rather than a strategic tool. It creates efficiencies on smaller parts of an over-arching marketing strategy and allows us to quickly remove inefficient parts of the marketing mix, or to take advantage of short-term opportunities. It also allows us to increase the amount of marketing investment when that money is shown to be working harder than usual. The overall marketing plan, though, should be driven by longer term in-depth insight work and certainly shouldn't be compromised by trying to make too many short-term tactical gains.

To make real-time planning work, we need data and we need to have done some prior analysis. Monitoring data series that start a debate when they change can be helpful, but it doesn't allow us to make rapid changes to a marketing schedule. An upfront investment in statistical modelling, so that we fully understand the data that we monitor, allows us to predict the likely outcomes of making a change to the marketing schedule.

Real-time planning is about investing in analysis and preparing for situations that could be faced in the future, and if you haven't done that prior analysis, then you're not ready for real-time planning.

As a small illustration of these principles in action, Brilliant Media has a retail client where analysis has revealed that strong online sales can be generated, by up-weighting search activity against a competitor's television schedule. The competitor TV activity is largely predictable and the benefits of diverting the online interest that it generates have been proven. As a result, competitor TV schedules are closely tracked and search terms up-weighted to take advantage of the spikes in search volumes that they generate. This adaptable schedule has real benefits in terms of additional sales and has arisen as a result of a piece of investigative analysis that identified data that was worth tracking and could be responded to very rapidly.

In the end, I would argue that real-time planning is something of a contradiction in terms. We shouldn't attempt to plan in real-time; we plan and we analyse, so that we can react in real-time.

Reproduced with permission of Admap, the world’s primary source of strategies for effective advertising, marketing and research. To subscribe visit © Copyright Admap.

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