When data fails marketing
The cliché ‘numbers do not lie’ is on trial. The truth that numbers tell is being questioned.
If indeed numbers never lie, Airtel Kenya should not be crying foul that its rival Safaricom is dominant.
If numbers do not lie, there should never be stories like those of individuals who burnt their fingers when they dived into quail farming about 10 years ago because of a perceived huge market.
Yet somehow, while there is some truth that numbers do not lie, that is not always how the market operates.
So, what happens when data fails? Or what should one do if they do not want to blindly depend on their data no matter how concrete or predictive it looks?
Justus Munyoki, a marketing lecturer and coordinator of research, publications and conferences at the University of Nairobi’s School of Business, acknowledges that there are instances when data can fail.
“It will depend on the method you used to collect the data and how you have designed data collection instruments,” he says.
If there is an issue with the data collection instruments, he explains, one ends up collecting data that is not relevant and accurate. This in turn gives a wrong prediction.
“It will fail you because you did not get data that is accurate, timely and reliable,” says Prof Munyoki.
If you use the data and in turn you realise the market is not responding as expected, he says, this calls for triangulation—a method used to increase the credibility and validity of research findings.
Triangulation is done by counter-checking the data collected with the relevant sources and looking at what others did latest on the same and their outcome.
“You can alternatively combine several other methods of collecting the same data and balance. They will support one another and enhance accuracy of that data,” says Munyoki.
Kenya is one of the markets where data might not be accurate when one is pushing a product. Numbers may clearly show where the market is going, yet that may not be the case.
It is something some firms have failed to understand, especially when they fail where others are succeeding.
For example, how can one explain using data how Shoprite, a South African retailer, failed to crack the Kenyan market and closed shop after recording a loss of Sh3 billion while Naivas and QuickMart are opening more branches across the country?
What is it with the Kenyan market that data cannot crack? The answer may be in Kenyans’ peculiar habits.
Former Safaricom chief executive Michael Joseph, now the board chair of the telco, observed this some years back when he concluded that Kenyans have peculiar calling habits.
Such habits include how one would rather buy five airtime scratch cards worth Sh50 each, rather than just buying one of Sh250.
Then there were many customers who used to wait until past 10pm to make calls because the rates were lower—beating the logic that communication is on need basis and cannot be delayed.
The answer to this puzzle could also lie in people’s culture, according to Alex Muiruri, regional managing director at marketing and branding firm Dentsu Location Services.
Mr Muiruri says while Kenyans are indeed peculiar, those are simply cultural nuances which every market has.
“You need to use the cultural nuances to make sure it works for you because that is the heartbeat of every market,” he says.
“Kenya has its own cultural nuances that you cannot really take away. It is culture and heritage and you have to work around it and shape it into a positive tangent rather than a negative.”
He notes that this is the same with other markets such as Uganda, Nigeria, Mozambique or South Africa.
Muiruri says while data forms a narration - for example informing one that 1,000 people like going to a particular location - this should just form the foundation of the decision-making process.
“At times not everything is led by data. There are times that you go by cultural insights,” he says.
“And there are things that you go outside the box and say, ‘despite this data saying this, we actually think that is wrong’.”
Muiruri says whenever Dentsu is coming up with a campaign, his team has to look at cultural insights and marry them with the set objectives of the campaign.
“If we are talking to a particular subset, we need to look at how they behave within a day,” he says.
The behaviour tracking could start from how the target group dresses, talks online, what it sees and the locations the group is interacting from. This leads to refined data.
Once this data has been refined, they decide how the messages can be contextualised. This can be done through mediums such as sports, comedy or memes.
This can also ride on technology such as mobile phones, which have grown to be part of people’s daily lives round the clock.
“How do we use that insight to making sure what you get to see is more of a positive message or something that will empower you?” poses Muiruri.