Many marketers have historically been rather spoilt from a data availability point of view. Whilst I know that many marketers out there survive with virtually no real data, on the whole the consumer marketing guys have always had more than their fair share. Even the most data starved marketer is typically feasting at the equivalent of an all-you-can-eat buffet compared to what trade marketers and key account managers might feed on in the same organization.
The world, or at least parts of it, is changing. More data is available than ever before. Social media is creating masses of data for consumer marketers to feed on, data sourced from retail point of sale and loyalty cards is finally bringing balance to the rest of the organization. Some managers probably slaver at the prospect, many I know quake in their boots. Whilst large organizations have the capability to create armies of analysts to sort through all of this, the smaller ones are much more challenged: Big data is changing the world of marketing in consumer goods – handing information is seen by CMOs as one of the biggest priorities for the future – so how should companies handle it?
How do I cope with all this data and what should I pay for?
High levels of data availability is arguably a good situation, but badly managed it can create chaos. Insight managers who actually spend 95% of their time crunching data, may not actually be spending enough time on creating genuine insight. Data crunching is daunting for many, and without a clear methodology to get through it all and create real insight at the end of the process; data paradigms are a looming danger: the data becomes everything, and everything else falls away. In other words, data actually narrows the view of the world rather than widening it. Expectations run high: be it from the CFO who is funding all of this, or from retailers who are sharing their data and expecting some return. This puts pressure on to produce, not quality, but quantity. Reports, analyses, more data (or the same data in a different way).
So if big data is “good” but it has lots of danger attached to its incorrect use, what should consumer and shopper marketers do about it?
Stop: Don’t go and get more data until you know what you are going to do with it. The expectations to create something (anything) from it, will rapidly outweigh the value it could create. The need to manage the data will rapidly become a task in and of itself.
Work out what you really need to know: Clear specific knowledge gaps create a much more specific brief, against which big data options can be weighed with other options.
Go back and review what you already have first: Most organizations use only a fraction of the data they currently have. There are many reasons for this, explored extensively here : but investing time and / or money in more data when existing data is not being exploited will rapidly reduce the ROI from these activities.
There’s no such thing as a free database: Free data isn’t free. Even if you get it ‘for free’ someone needs to manage it, and analyze it, and make reports. Someone (at least some of the time) has to read those reports. Further, data is sourced from someone: they have put effort in and, if there is no return, eventually they will quit. At an obvious level, a retailer who supplies data to a manufacturer does so with (either explicit or implicit) expectations that the manufacturer will do something with that data which will add value to the retailer and create a return. Every consumer investing in a brand relationship via social media, expects some return.
Review regularly: Once you are started, check frequently and regularly to establish what the returns are. Those reports that are being run – are they adding value? Our star insight manager: are they actually spending enough time thinking, or are they too busy crunching?
Focus on the outcome: Having lots of data creates bills, it doesn’t pay them. Analysis does not create value. It is the insight which, upon implementation, creates value. Measure not the fabulous facts you now have, but the value which can be directly attributed to it (‘without that data we would never have…”). If you haven’t used that sentence about a particular source of data, question why you still have it.
Big Data is clearly a blessing: but managed badly it is a Pandora’s box of a curse. It is impossible to avoid, and a clear strategy on how to handle it, how to get the best out of it, and how to deliver a meaningful ROI from the investment will mark out the most successful marketers over the next decade.
How are you approaching this challenge? Which data is most useful and valuable to your organization? What has been a complete waste of time and money?
