Brand building is seeing a resurgence in a digital age where marketers are struggling between justifying ROI on digital while continuing to learn/ optimize using the limited marketing budgets in an extensively competitive environment.
 
Two events significantly influence the resurgence of brand building:
1. Viewability and
2. Ad Blocking
 
1. Viewability
Marc Pritchard (CMO – P&G) at Dmexco 2017 noted that average view times for digital ads is 1.7 seconds while only 20% of digital ads are viewed beyond 2 seconds. Viewability is amongst the Top 2 concerns of digital media planners in the US.
 
2. Ad Blocking
Ad Blocking user penetration in the US is set to double in 4 years (15.7% in 2014 to 30.1% in 2018). The number for Millenials (age 25-34) is even higher at 40%.
 
How can marketers create a long term strategy to respond to the marketplace challenges ?  

Build relevance. A brand that embodies values that resonate with its targetted consumers, a brand that builds interest Vs interrupts and is sensitive to build utility around every engagement with its consumers.
 
Its interesting to see a successful company like AirBnB ($58 billion in market capitalisation) make an effort to build its brand. Nancy King (AirBnB) and Neil Barrie (TBWA/ Chiat/ Day) in an interview with FastCompany reviews the companys decision to actively build a brand. For a mega successful company in Silicon Valley to acknowledge that it needs to build its brand over the long term in a disciplined manner is in itself a remarkable break-through. Additionally, the logic for investing in brand building is sound and in many ways is applicable across categories for any brand. The need for marketers to build a brand continues to be a priority, inextricably linked with every digital investment journey they are making.
 
To paraphrase their step-by-step method (which I find applicable for every brand) :

1. Need to Scale : Appeal to a broader target audience, beyond Millenials
2. Ignite Behavior Change: Help people who are unused to AirBnB adopt new behaviors for their travel stay needs
3. Build Emotional Relevance : Build trust by connecting on shared values (amongst non-Millennials)
4. Invite Preference : Help people choose AirBnB Vs Expedia  

 
Brands are beginning to realise that the problems they face in combatting the challenges in digital marketing (Transparency, Attribution, Ad Blocking etc) are not going to go away soon. And that building their brand, being consumer centric is only Step 1 of winning in the long term.

“Big data is about finding correlation, while small data is about finding causation” – Martin Lindstorm.
 
Marketing is obsessed with harnessing the power of big data – rightly so. Big data is extremely valuable in understanding patterns at scale, helping brands take advantage of trends and for marketers to drive better ROI. But this laudable effort should be done in conjunction with the reverance for ‘small data’.
 
Small data and the role of insights in marketing is different from that of big data and provides valuable inputs into marketing that big data, by definition, cannot. 

1. “Why” is the single biggest question that big data cannot answer and requires small data. For example "Churn Prevention" is one of the most common applications of big data. Predicting patterns of behavior of customers who have churned helps create 'flags' that signal when the behaviours occur for other customers. But this does not solve for why churn may be occurring in the first place. To solve for this we need to understand the context of the churn behaviour. If the behaviour pre-churn is identified at scale is a complaint registered then the nature of the complaints might point to a solution to prevent churn. If the pre churn behaviour is isolated to certain segments/ life-stages then it might make sense to determine if the organisation is primed to serve customers when they move life-stages.   
2. Big Ideas come from Small Data : Observing people using your product, talking to them about your product and how it intersects with their lives can help you discover new ideas for your brand that when scaled can become an enduring asset. 
3. Small Data Feeds Big Data : Sometimes micro observations are important to understand tipping points. A high abandonment rate on a website can be captured, analysed ad infinitum by big data. But a survey conducted specific to understanding the reasons and nuances of abandonment can be more revealing and effective. Built over a series of such events small data improvements/ hypotheses can be scaled into big data observations and rules.

 
Small data and big data are both valuable but different. Using both intelligently Vs choosing one should be the way to go. Small data should feed valuable hypotheses/ ideas and inspire while big data should discern patterns @ scale and inform / prioritise.​