My musings on brands

Using data as a strategic brand asset

Every organisation today is sitting on a pile of data about its consumers. But nothing is enough and there is a relentless pursuit to accumulate more and more data. There is a data deluge, which has led to monikers like "data is the new oil" (which in itself is a misnomer). As modes of consumer interaction proliferate, so has the amount of data getting generated. 

To what extent is this gargantuan amount of data insightful or actionable, is the most important question?

Increasingly, organisations are getting stuck in the middle of two opposing forces - the need to make sense of an ever increasing pile of data and the need to simplify their business operations. Having too much of the very source that can guide strategic brand decisions is not a good thing. In most cases, brand managers struggle to fit the 'data' at one end of the funnel, when the aim is to get effective strategies out at the other end.

It is like a "drain-block" scenario, wherein too much substance has been forced through a narrow drain opening, resulting in clogging.

Before getting deeper into the drain-block scenario, let's give brand managers the benefit of doubt that all sporadic and continuous pursuits of data for brands in the portfolio is a meaningful endeavour. In a pragmatic sense, you cannot build strong brands without data. 

Are there opportunities to channelise these continuous data pursuits into meaningful and impactful strategies? Can these heaps of data be transformed into actionable brand building strategies? Can brand data be effectively mined to reveal nuggets of golden consumer understanding?

The answer is a resounding "yes". The critical question then is why organisations consistently fail to activate data to create impactful brand strategies. The fault does not lie in the amount of data that is being collected and also not in its nature. The fault lies in "why" it is being collected. 

If we were to put 10 data metrics in front of a brand builder and ask why each of them are collected and monitored, for 6-7 out of these 10 metrics the answer will be incomplete, vague, confused or simply irrelevant.

The biggest challenge faced by organisations that have a data-driven approach towards brand building, is the fragmented and disparate nature of data collection. For global organisations with global brands the scenario is even darker, with each country doing its own thing. Things start to improve when strong and consistent global brand building principles are implemented. But it gives birth to the "global vs. local" debate, with arguments and counter-arguments flying around. 

This is equally true in both centralised and decentralised organisational structures, both in terms of hierarchy and decision-making. In most organisations, where there are separate business units responsible for different parts of the product / brand portfolio, data collection and its accumulation happens in a “the left hand does not know what the right hand is doing” manner. Whenever a organisation embarks on a harmonisation programme focused on brand building, the first level to align is the different ways of looking at the same thing (i.e. brand performance). In most cases this disjointed and fragmented way of looking at data limits its actionability. Consequently, a global organisation with global data for its brands cannot formulate an effective global brand building strategy.

In a piece of analysis conducted and published in 2013 by McKinsey & Company, one of the key tenets behind an effective data-driven strategy was having the ability to creatively select the right data from the available pool. McKinsey also observed in its analysis — “Often, companies already have the data they need to tackle business problems, but managers simply don’t know how they can use this information to make key decisions.”

The diverse modes of collecting data, its form, its volume and the original reason why it was collected impacts brand strategy at all levels. Here are a few examples of how existing data can be used to develop strategy without having to collect anew:

  • Category: Lets assume there are six countries in the global organisation matrix who have gone ahead and done their own segmentation work. The objective was to identify local market opportunities for effective portfolio deployment. Without having the need to conduct a new global one, these six disparate segmentations can be bought together (fused) through analytical techniques to create a global opportunity matrix for portfolio rationalisation and development
  • Brand: If you have a global brand with different levels of maturity in different markets and have been tracking its performance over a period of time, then finding smart ways of comparing its performance across markets is a strategic use of existing data. This avoids an expensive overhaul of existing brand tracking programmes
  • Advertising: Global and local creative development strategies always come into loggerheads in global organisations. The debates don’t end at creative development but continue on to their effectiveness measurement. Disparities in measurement frameworks (and resultant metrics) make global creative strategies difficult to implement. But by going deep into local level insights, the creative development process can be developed with a localisation objective. Measurement frameworks, even though fragmented, can still be harmonised to find common truths

Some of these examples have been simplified to illustrate ways of using existing data strategically. In the real world, using existing data effectively will be a much more complex endeavour. The objective remains unchanged - leveraging existing data assets in a stronger manner. It is always more efficient and productive when organisations maximise the potential of existing data (and not add to the stockpile).

A critical thing to keep in mind is the fact that ‘strategy is forward-looking while data is backward-looking’. Predictive analytics is still a fledging body and can only become more accurate when the inputs going into the models have been creatively and strategically selected.

Effective and creative use of existing data assets requires marketers to adopt new behavioural traits, all of which are supposed to challenge traditional mindsets towards data:

  • Control the habit of ‘asking’ or ‘commissioning’: Every strategic question does not require asking more ‘new’ questions or requesting for more data or commissioning new projects. One of the biggest success factors around gaining visibility and authority on social media is the ability to do excellent curation. The same principle can be applied to answer new strategic questions / challenges. Curate existing pieces of strategic work, conduct due diligence of recommendations given by your key strategic partners and dust off some of the reports that have been accumulating on your desk
  • Challenge the definition of ‘new’: As mentioned before, adding to the data stockpile is often a result of questions that are perceived to be ‘new’, while in reality these are questions that probably has been asked in a different form in the past. Strongly challenge the definition of ‘new’ when a question comes attached with a new data request

Let’s revisit the definition of “new”:

  • produced, introduced, or discovered recently or now for the first time; not existing before - "A new Madonna album"
  • already existing but seen, experienced, or acquired recently or now for the first time - "her new bike"

Constantly challenge the definition of “new” by reverting to the possibility that it can already exist and is just being presented in a different form

  • Let go of your ‘data crutches’: In a lot of instances, answers to challenging or vexing strategic questions requires fresh interpretation of existing data or a new way of thinking. This should be the starting point. It should never ever start by a request for new data. Marketers should realise the fact that consumers and markets will never evolve or change with a speed akin to programmatic ad placement. Letting go of your ‘data crutches’ does not mean turning a blind eye to an asset that you can mine. It essentially means stop asking for new data whenever a question is posed to you
  • Push back on KPIs: The acronym Key Performance Indicators has the word ‘Key’ in it, which points towards focus. Brand managers should relentlessly push back on an increasing list of KPIs (the moment they are more than 5, the acronym in itself loses value). Lesser the KPIs, lesser is the need for data and more importantly, lesser is the probability of getting paralysed by data anxiety

    If you have had successful product launches in the past, analyse how many KPIs were used to monitor product performance across the innovation lifecycle. If you have had successful advertising campaigns in the past, understand the KPIs on which their success was measured. You will be surprised on how few they are. Apprehension and lack of confidence influences the use of ‘data crutches’, which in turn warrants the need for more KPIs. This leads to more data being collected and the cycle continues

  • Instil an attitude that hastens ‘redundancy’: This can be the most controversial of all, and can only come through a thorough understanding of different types of data and their usefulness. Decision-makers should have confidence to shoot down unnecessary, time-consuming, cyclical, open-ended and fragmented data requests. Such kind of requests (whether added to new or existing questions) just adds to ‘mining’ time and does not inform or influence strategy

A constant ‘redundancy’ process can be quite useful. Every piece of data collection exercise should go through a due-diligence process that identifies all possible uses. Anything for which it cannot be used shouldn’t be part of any request (either internally or from external sources). Decision-makers should also follow an ‘expiry date’ principle. Unless very strategic in nature, every piece of data should have an expiry date. If a similar request comes before an asset’s expiry date, then the new request should be immediately rejected

For the data-insights-strategy funnel to work effectively, we need to minimise the amount of information flowing through the funnel. This can only happen through a relentless process of sieving and throwing out and letting only the ‘diamonds’ live within the organisation. Yes, tactical data is critical for making short-term decisions, but we need to make sure that it is used for what it is supposed to solve (and every piece of data request does not become a tactical one).