Alex Steer

Advertising effectiveness, analytics and strategy / about / archive

Finding business models in big data

462 words

In July last year, at the height of the big data nonsense surrounding the doomed Omnicom/Publicis merger, I wrote this about the overused notion that 'it's what you do with the data that counts':

Everybody with a bit of common sense in marketing knows this, and could tell you where and when they want to use data more – when identifying specific challenges and opportunities, when taking the temperature of an issue and figuring out a response in real time, when measuring and adapting the performance of creative content mid-stream during a campaign, and when measuring the relationship between short-term activity and long-term brand value and behavioural change.

I think it's still true, and I think this is a crunch time for the firms that flooded the marketing industry selling the dream of big data. They're maturing, they need to get out from under the wing of their startup funders, and that means they need to start developing proper business models that attract customers and generate revenue.

Based on what I wrote above, I think there are four business models where big data will make a serious impact on the marketing industry. They are:

  1. Adding good-quality behavioural data into the econometric models used occasionally in strategic planning.
  2. Supplying metrics which can be used to measure and improve the ongoing performance of advertising content and media.
  3. Supplying metrics as inputs to advertising effectiveness research and brand tracking.
  4. Increasing the speed and breadth of information-gathering during crisis/reputation management situations.

None of these things - marketing strategy, media measurement, advertising research and crisis management - is new. Extra data and new technologies improve them rather than transforming them. Because of that, the data has to be additive: it needs to let marketers see or do what they saw or did previously, but better.

The winners are likely to be organisations who understand what data marketers already have, and how they use it: management consultancies, media agencies, and research agencies in particular. The most successful start-ups will target these relentlessly and concentrate on being useful rather than sounding smart. This is likely to be a bumpy transition for firms who have thrived on VC money rather than customer revenue for so long.

As the market matures, biggest red herrings will be the things that today sound the whizziest: ad-hoc predictive modelling, sentiment analysis, customer lifecycle modelling and automated adaptive advertising. All of these sound (and are) extremely smart, but they're a bad fit for what brand marketers want to be able to know and do, and how consumers behave in most categories. They make for a good sales pitch but a high-risk business model.

# Alex Steer (15/06/2014)